既存モデルを参照してアライメント

1 files changed, 12 insertions, 7 deletions

diff --git a/scripts/voice_conversion_test.py b/scripts/voice_conversion_test.py
index 24982ea..43c66d5 100644
--- a/scripts/voice_conversion_test.py
+++ b/scripts/voice_conversion_test.py
@@ -8,7 +8,7 @@ from pathlib import Path
 import librosa
 import numpy
 
-from become_yukarin import VoiceChanger
+from become_yukarin import AcousticConverter
 from become_yukarin.config.config import create_from_json as create_config
 
 parser = argparse.ArgumentParser()
@@ -16,10 +16,12 @@ parser.add_argument('model_names', nargs='+')
 parser.add_argument('-md', '--model_directory', type=Path, default=Path('/mnt/dwango/hiroshiba/become-yukarin/'))
 parser.add_argument('-iwd', '--input_wave_directory', type=Path,
                     default=Path('/mnt/dwango/hiroshiba/become-yukarin/dataset/hiho-wave/hiho-pause-atr503-subset/'))
+parser.add_argument('-g', '--gpu', type=int)
 args = parser.parse_args()
 
 model_directory = args.model_directory  # type: Path
 input_wave_directory = args.input_wave_directory  # type: Path
+gpu = args.gpu
 
 paths_test = list(Path('./test_data/').glob('*.wav'))
 
@@ -29,12 +31,12 @@ def extract_number(f):
     return int(s[-1]) if s else -1
 
 
-def process(p: Path, voice_changer: VoiceChanger):
+def process(p: Path, acoustic_converter: AcousticConverter):
     try:
         if p.suffix in ['.npy', '.npz']:
             p = glob.glob(str(input_wave_directory / p.stem) + '.*')[0]
             p = Path(p)
-        wave = voice_changer(p)
+        wave = acoustic_converter(p)
         librosa.output.write_wav(str(output / p.stem) + '.wav', wave.wave, wave.sampling_rate, norm=True)
     except:
         import traceback
@@ -54,13 +56,16 @@ for model_name in args.model_names:
     model_paths = base_model.glob('predictor*.npz')
     model_path = list(sorted(model_paths, key=extract_number))[-1]
     print(model_path)
-    voice_changer = VoiceChanger(config, model_path)
+    acoustic_converter = AcousticConverter(config, model_path, gpu=gpu)
 
     output = Path('./output').absolute() / base_model.name
     output.mkdir(exist_ok=True)
 
     paths = [path_train, path_test] + paths_test
 
-    process_partial = partial(process, voice_changer=voice_changer)
-    pool = multiprocessing.Pool()
-    pool.map(process_partial, paths)
+    process_partial = partial(process, acoustic_converter=acoustic_converter)
+    if gpu is None:
+        pool = multiprocessing.Pool()
+        pool.map(process_partial, paths)
+    else:
+        list(map(process_partial, paths))